Analyzing the use of X to communicate vaccine rollout in Region of Peel (Ontario, Canada): A Multimethod Approach (Preprint)
Bibliographic record
Abstract
BACKGROUND Social media can facilitate community engagement and promote public trust but also fuel public mistrust. In the early stages of the COVID-19 pandemic, some areas in Ontario were identified as COVID-19 hotspots, where infection rates were higher than the provincial average. Several hotspots were comprised of neighborhoods with ethnoracially minoritized populations who are often precariously employed and housed, making them more vulnerable to acquiring COVID-19. OBJECTIVE This study aimed to characterize and assess the ways in which social media was used by public health officials and community partners to reach communities and build vaccine confidence in Region of Peel. METHODS A multimethod approach was employed to examine vaccine-related communication practices on social media platform X (formerly Twitter). Three methods—trend analysis, qualitative content analysis, and descriptive analysis were used. Data collection was conducted from December 2020 to November 2021 using Brandwatch™ to collect and annotate COVID-19 related tweets from Region of Peel and its 129 Mass Vaccination Program (MVP) partners throughout three phases of the vaccine rollout. To determine how Region of Peel used X during vaccine rollout, 24,637 tweets were automatically annotated to identify its lead agency, the vaccine locations that the tweets were promoting, the tweets’ intended priority populations, and whether tweets contained tailored messages to faith-based communities. RESULTS Peel utilized X as a medium for public service announcements (PSAs) to provide real-time information to the general public. Peel and its MVP partners (e.g., community health centers, other public sector and non-profit organizations) primarily focused on creating tweets to engage Indigenous and Black communities. The main themes addressed public questions about vaccine efficacy, safety, and eligibility per provincial guidelines, booking procedures, and vaccine availability. Black and South Asian community-based organizations (CBOs) were Peel’s most active partners on X. However, partners serving Punjabi and Hindu communities had limited visibility on X. Among faith-oriented CBOs, Muslim organizations were the most prominent in sharing religious messaging. CONCLUSIONS Peel leveraged existing relationships with partners to engage a broad audience about COVID-19 vaccines; its use of X demonstrated how strategic communication and partnerships can improve accessibility to vaccine information to ethnoracially minoritized and faith-based groups. The findings underscore the value of integrating social media with broader public health strategies to build trust and enhance engagement of diverse populations.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".